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Détail de l'auteur
Auteur Mahesh Pal
Documents disponibles écrits par cet auteur
Affiner la rechercheModeling pile capacity using support vector machines and generalized regression neural network / Mahesh Pal in Journal of geotechnical and geoenvironmental engineering, Vol. 134 N°7 (Juillet 2008)
in Journal of geotechnical and geoenvironmental engineering > Vol. 134 N°7 (Juillet 2008) . - pp. 1021–1024
Titre : Modeling pile capacity using support vector machines and generalized regression neural network Type de document : texte imprimé Auteurs : Mahesh Pal, Auteur ; Surinder Deswal, Auteur Année de publication : 2008 Article en page(s) : pp. 1021–1024 Note générale : Geotechnical and geoenvironmental engineering Langues : Anglais (eng) Mots-clés : Piles Neural networks Vector analysis Stress wave Résumé : This note investigates the potential of support vector machines based regression approach to model the static pile capacity from dynamic stress-wave data. A data set of 105 prestressed precast high strength concrete spun pipe piles is used. Radial basis function and polynomial kernel based support vector machines were used to model the total pile capacity and results were compared with a generalized regression neural network approach. A total of 81 data set were used to train, whereas the remaining 24 data sets were used to test the created model. A correlation coefficient value of 0.977 was achieved by generalized regression neural network in comparison to values of 0.967 and 0.964 achieved by radial basis function and polynomial kernel based support vector machines, respectively. Results suggest an improved performance by generalized regression neural network based approach in comparison to support vector machines but polynomial kernel based support vector machines provide a linear relationship to predict total pile capacity using stress-wave data. En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%291090-0241%282008%29134%3A7%2810 [...] [article] Modeling pile capacity using support vector machines and generalized regression neural network [texte imprimé] / Mahesh Pal, Auteur ; Surinder Deswal, Auteur . - 2008 . - pp. 1021–1024.
Geotechnical and geoenvironmental engineering
Langues : Anglais (eng)
in Journal of geotechnical and geoenvironmental engineering > Vol. 134 N°7 (Juillet 2008) . - pp. 1021–1024
Mots-clés : Piles Neural networks Vector analysis Stress wave Résumé : This note investigates the potential of support vector machines based regression approach to model the static pile capacity from dynamic stress-wave data. A data set of 105 prestressed precast high strength concrete spun pipe piles is used. Radial basis function and polynomial kernel based support vector machines were used to model the total pile capacity and results were compared with a generalized regression neural network approach. A total of 81 data set were used to train, whereas the remaining 24 data sets were used to test the created model. A correlation coefficient value of 0.977 was achieved by generalized regression neural network in comparison to values of 0.967 and 0.964 achieved by radial basis function and polynomial kernel based support vector machines, respectively. Results suggest an improved performance by generalized regression neural network based approach in comparison to support vector machines but polynomial kernel based support vector machines provide a linear relationship to predict total pile capacity using stress-wave data. En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%291090-0241%282008%29134%3A7%2810 [...]
in Journal of geotechnical and geoenvironmental engineering > Vol. 134 N°7 (Juillet 2008) . - pp. 1021–1024
Titre : Modeling pile capacity using support vector machines and generalized regression neural network Type de document : texte imprimé Auteurs : Mahesh Pal, Auteur ; Surinder Deswal, Auteur Année de publication : 2008 Article en page(s) : pp. 1021–1024 Note générale : Geotechnical and geoenvironmental engineering Langues : Anglais (eng) Mots-clés : Piles Neural networks Vector analysis Stress wave Résumé : This note investigates the potential of support vector machines based regression approach to model the static pile capacity from dynamic stress-wave data. A data set of 105 prestressed precast high strength concrete spun pipe piles is used. Radial basis function and polynomial kernel based support vector machines were used to model the total pile capacity and results were compared with a generalized regression neural network approach. A total of 81 data set were used to train, whereas the remaining 24 data sets were used to test the created model. A correlation coefficient value of 0.977 was achieved by generalized regression neural network in comparison to values of 0.967 and 0.964 achieved by radial basis function and polynomial kernel based support vector machines, respectively. Results suggest an improved performance by generalized regression neural network based approach in comparison to support vector machines but polynomial kernel based support vector machines provide a linear relationship to predict total pile capacity using stress-wave data. En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%291090-0241%282008%29134%3A7%2810 [...] [article] Modeling pile capacity using support vector machines and generalized regression neural network [texte imprimé] / Mahesh Pal, Auteur ; Surinder Deswal, Auteur . - 2008 . - pp. 1021–1024.
Geotechnical and geoenvironmental engineering
Langues : Anglais (eng)
in Journal of geotechnical and geoenvironmental engineering > Vol. 134 N°7 (Juillet 2008) . - pp. 1021–1024
Mots-clés : Piles Neural networks Vector analysis Stress wave Résumé : This note investigates the potential of support vector machines based regression approach to model the static pile capacity from dynamic stress-wave data. A data set of 105 prestressed precast high strength concrete spun pipe piles is used. Radial basis function and polynomial kernel based support vector machines were used to model the total pile capacity and results were compared with a generalized regression neural network approach. A total of 81 data set were used to train, whereas the remaining 24 data sets were used to test the created model. A correlation coefficient value of 0.977 was achieved by generalized regression neural network in comparison to values of 0.967 and 0.964 achieved by radial basis function and polynomial kernel based support vector machines, respectively. Results suggest an improved performance by generalized regression neural network based approach in comparison to support vector machines but polynomial kernel based support vector machines provide a linear relationship to predict total pile capacity using stress-wave data. En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%291090-0241%282008%29134%3A7%2810 [...]
